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            "Development Status :: 4 - Beta",
            "Environment :: MacOS X",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "# dtreeplt\nit draws Decision Tree not using Graphviz, but only matplotlib.  \nIf `interactive == True`, it draws Interactive Decision Tree on Notebook.\n\n## Output Image using proposed method: dtreeplt (using only matplotlib)\n![graphviz](output/result.png)\n\n## Output Image using conventional method: export_graphviz (Using Graphviz)\n![graphviz](output/using_graphviz.png)\n\n## Output Image using dtreeplt Interactive Decision Tree  \n\n![graphviz](output/idt_demo.gif)\n\n## Installation\nIf you want to use the latest version, please use them on git.  \n\n`pip install git+https://github.com/nekoumei/dtreeplt.git`\n\nwhen it comes to update, command like below. \n\n `pip install git+https://github.com/nekoumei/dtreeplt.git -U`\n\n\nRequirements: see requirements.txt    \nPython 3.6.X.\n\n## Usage\n### Quick Start\n```python\nfrom dtreeplt import dtreeplt\ndtree = dtreeplt()\ndtree.view()\n# If you want to use interactive mode, set the parameter like below.\n# dtree.view(interactive=True)\n\n```\n### Using trained DecisionTreeClassifier\n```python\n# You should prepare trained model,feature_names, target_names.\n# in this example, use iris datasets.\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom dtreeplt import dtreeplt\n\niris = load_iris()\nmodel = DecisionTreeClassifier()\nmodel.fit(iris.data, iris.target)\n\ndtree = dtreeplt(\n    model=model,\n    feature_names=iris.feature_names,\n    target_names=iris.target_names\n)\nfig = dtree.view()\n#if you want save figure, use savefig method in returned figure object.\n#fig.savefig('output.png')\n```\n\n\n\n\n",
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